构建芬兰语农业决策支持RAG系统，提升低资源语言领域问答质量。
Towards AI Evaluation in Domain-Specific RAG Systems: The AgriHubi Case Study
- 用芬兰农业文档+开源PORO模型构建RAG系统，结合溯源与用户反馈迭代优化。
- 用户研究显示答案完整度、语言准确性和可信度显著提升，大模型延迟更高。
- 为低资源语言领域RAG系统设计提供实证参考，适合农业AI开发者使用。
大语言模型在知识密集型领域有潜力，但在农业领域受限于弱事实依据、以英语为主训练数据及缺乏真实世界评估。这些问题在低资源语言中尤为突出，尽管高质量领域文档存在，却难以通过通用模型访问。本文提出AgriHubi，一个面向芬兰语农业决策支持的领域自适应检索增强生成（RAG）系统。该系统整合芬兰农业文档与开放的PORO系列模型，结合显式来源溯源与用户反馈实现迭代优化。经过八轮开发与两次用户研究验证，系统在答案完整性、语言准确性及感知可靠性方面均有明显提升。结果还揭示了部署更大模型时响应质量与延迟之间的实际权衡。本研究为低资源语言环境下领域特定RAG系统的构建与评估提供了实证指导。
原文摘要 · Abstract (English)
Large language models show promise for knowledge-intensive domains, yet their use in agriculture is constrained by weak grounding, English-centric training data, and limited real-world evaluation. These issues are amplified for low-resource languages, where high-quality domain documentation exists but remains difficult to access through general-purpose models. This paper presents AgriHubi, a domain-adapted retrieval-augmented generation (RAG) system for Finnish-language agricultural decision support. AgriHubi integrates Finnish agricultural documents with open PORO family models and combines explicit source grounding with user feedback to support iterative refinement. Developed over eight iterations and evaluated through two user studies, the system shows clear gains in answer completeness, linguistic accuracy, and perceived reliability. The results also reveal practical trade-offs between response quality and latency when deploying larger models. This study provides empirical guidance for designing and evaluating domain-specific RAG systems in low-resource language settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。